A Reusable Commitment Management Service using Semantic Web Technology


Nov 5, 2013 (3 years and 5 months ago)


A Reusable Commitment Management Service using Semantic Web
Alun Preece,Stuart Chalmers,and Craig McKenzie
University of Aberdeen,Computing Science,Aberdeen,UK
Abstract.Commitment management is a key issue in service-provisioning in the
context of virtual organisations (VOs).Aservice-provider —which may be a sin-
gle agent acting within an organisation,or the VOacting as a collective whole —
manages particular resources,and commits these resources to meet specific goals.
Commitments can be modelled as constraints on resources.Such constraints are
often soft:they can be broken if necessary.The goal of the work described in
this paper is to create an open,reusable commitment management service (CMS)
based on Semantic Web standards.The chief requirement is that the CMS should
be reusable in different domains,able to manage commitments over services de-
scribed in a wide range of domain-specific service ontologies.This paper presents
open Semantic Web representations for (1) expressing individual commitments as
constraints over service descriptions,(2) capturing a set of commitments as a soft
constraint satisfaction problem,and (3) representing and communicating the so-
lution to a soft CSP.A reference implementation of a constraint solver able to
operate on (1) and (2) to produce (3) is described,and its reuse is demonstrated
in two distinct domains:e-commerce and e-response.
1 Introduction
Commitment management is a key issue in service-provisioning in the context of virtual
organisations (VOs).A service-provider —which may be a single agent acting within
an organisation,or the VOacting as a collective whole —manages particular resources,
and commits these resources to meet specific goals.The issue of commitment manage-
ment appears throughout the lifecycle of such organisations [14]:when a partner is
deliberating whether to bid to join a VO,it must consider its existing commitments,
and construct a bid that is compatible with its commitments;when a VOis operating,it
must manage its commitments over its collective resources and —when perturbations
inevitably occur — the VO must adapt by revising its commitments;finally,when a
VO’s job is done and it disbands,commitments must be released and cleaned-up.Al-
though the types of services managed in each case are very diverse,commitment man-
agement issues arise in VOs in all domains,including e-commerce [13],e-science [6],
and e-response
Often,the commitment of resources to goals is governed by service-level agree-
ments.The commitments can be modelled as constraints on the resources.Such con-
straints are often soft:they can be broken if necessary [4].When a service-provider is
presented with a new potential commitment,it must perform reasoning to determine if
it can take on this commitment,possibly by dropping (breaking) existing commitments
(constraints).Hence,the goal of the agent’s deliberation procedure becomes to find an
optimal solution that satisfies a maximal subset of the constraints [7].In this context,
constraints often have associated utility values,indicating the relative importance of sat-
isfying individual constraints or clauses [2,8].Importantly,these utilities are generally
not absolute:they are relative to the particular constraint satisfaction problem(CSP) in
which the constraint is being applied.In relation to a particular solution,a given con-
straint may be satisfied or violated,and it is often useful to be able to represent and
reason about which constraints are satisfied/violated by a given solution [5].The ability
to make statements about whether a constraint is satisfied or not in a given context is
commonly called constraint reification.
The goal of the work described in this paper is to create an open,reusable com-
mitment management service (CMS) based on Semantic Web standards.The chief re-
quirement is that the CMS should be reusable in different domains,able to manage
commitments over services described in a wide range of domain-specific service on-
tologies.This requirement motivates the Semantic Web approach:it is our expectation
that the majority of service ontologies will be defined in a SW-based representation,
currently OWL or RDFS.
By rooting our CMS in the Web and Semantic Web architec-
tures,we also exploit existing XML-based interchange formats (including RDF syntax),
transport protocols (HTTP,SOAP,etc) and logical foundations (including description
logic and rules).Building on these foundations,the requirements for our CMS are:
1.an open format for expressing individual commitments as constraints over service
2.an open format for capturing a set of commitments as a soft constraint satisfaction
3.an open format for representing and communicating the solution to a soft CSP;
4.a reference implementation of a constraint solver able to operate on (1) and (2) to
produce (3);
5.demonstrations of the CMS working in at least two distinct domains,to provide
proof-of-concept of reusability.
In light of these requirements,this paper offers the following:
– We review our Semantic Web Constraint Interchange Format (CIF) [12],which
builds on the proposed Semantic Web Rule Language (SWRL) [9].This format
(CIF/SWRL) provides an open representation for expressing individual commit-
ments as quantified constraints over service descriptions defined in terms of OWL
or RDFS ontologies.
– We present an ontology for representing soft CSPs and their solutions.The ontology
—which is intended to complement CIF/SWRL but is also potentially usable with
other constraint and rule representations —allows utility values to be associated
with constraint expressions.The solution format allows constraints to be labelled
to indicate whether they are satisfied or not in a particular solution.
For example,OWL-S (http://www.daml.org/services) or WSML (http://www.wsmo.org).
– We describe our reference implementation of a constraint solver based on the Java
Constraint Library (JCL)
,and present two demonstration systems using the CMS,
one in an e-commerce domain (multimedia service provisioning) and the other in
an e-response domain (disaster management).
The paper is organised as follows:Section 2 presents an abstract scenario involv-
ing an agent reasoning about it’s commitments using constraint solving,motivating
the need to represent utility values and constraint reification;Section 3 describes our
the CIF/SWRL constraint interchange format;Section 4 surveys approaches to han-
dling soft and reified constraints in various CSP-solving frameworks,and describes our
ontology for representing soft CSPs;Section 5 describes the two virtual organisation
demonstrator implementations;Section 6 provides discussion and conclusion.
2 Managing Commitments as Constraints
To illustrate the use of soft constraints for modelling and managing commitments,we
nowpresent a detailed example.This example is a simplification of the type of problem
that occurs in virtual organisation service-provisioning application domains.
Consider two service-providing agents,a1 and a2.Each agent can provide a certain
amount of resource x (12 units from a1 and 10 from a2).The agents have existing
commitments —c1,c2 and c3 on those resources,as shown in the first schedule in
Figure 1:
– c1:5x fromtime 0→5 on a1
– c2:3x fromtime 6→10 on a1
– c3:5x fromtime 0→7 on a2
Note that in this simple example we only look at a single type of resource (x).How-
ever,the solution to the commitment management problem presented here generalises
to any number of resource types and combination [5].We restrict ourselves to a single
resource type here only for the sake of clarity.
If a new request,N is received by the agents to provide 15x from time 0→10,then
the agent has four main choices:
– Reject N and satisfy existing commitments c1,c2 &c3 (Schedule 1 in Figure 1)
– Accept N and break c1 &c2 (Schedule 2)
– Accept N and break c3 (Schedule 3)
– Accept N and break c1 &c3 (Schedule 4)
(Note that there are many permutations of the exact amounts of the resource x,but in
terms of commitments satisfied or broken these are the four main choices.)
As the number of agents and commitments increases the number of possible com-
binations of solutions that satisfy all the commitments (and solutions that break com-
mitments) grows exponentially.Also the number of trivial solutions (i.e.solutions that
vary in extremely small detail) increases (e.g.schedule 3 could take 7x from a1 and
5x 0->5
5x 0->7
5x 0->5
5x 0->7
5x 0->5
5x 0->7
resources contributed
to new commitment
Schedule 2
Break c1&c2.
Satisfy N
(a:12x, a2:3x)
Schedule 3
Break c3
Satisfy N
Schedule 4
Break c1&c3
satisfy N
(a1:12x - 0->6
9x - 6->10
a2:3x - 0->6
6x - 6->10)
3x 6->10
3x 6->10
3x 6->10
5x 0->5
3x 6->10
5x 0->7
Schedule 1
Reject N
Satisfy c1,c2&c3
Fig.1.Agent a1 &a2’s options for providing new commitment N
8x froma2 rather than 5x and 10x which would not affect the commitments broken).
The main emphasis behind the CSP-solving procedure is to find solutions that break
commitments (i.e.solutions that are different enough in outcome that they break differ-
ent commitments).As a result of this we need to equip the CSP solver with a method
for differentiating between solutions.We also need a way to prioritise commitments so
that we can rule out solutions that break commitments that have been specified a priori
as ‘must-complete’ tasks.
This kind of commitment management mechanismcan be implemented as a reifica-
tion extension to a cumulative scheduling CSP solver that uses a combination of reifi-
cation and constraint value labeling to provide the required commitment management
and prioritisation — details are provided in [5],and further discussion of our virtual
organisation demonstrator implementations appears in Section 5.
3 A Constraint Interchange Format Based on SWRL
Our Constraint Interchange Format (CIF) is derived from the Colan [1] constraint lan-
guage,which is based on range restricted first order logic.
Earlier versions of the CIF
language were aligned with RDF [11] and SWRL [12].CIF constraints are essentially
defined as quantified implications,for example:
(∀?x∈X,?y∈Y) p(?x,?y) ∧ Q(?x) ⇒
(∀?z∈Z) q(?x,?z) ∧ R(?z) ⇒
(∃?v∈V) s(?y,?v)
Commitment c2 fromthe example in Section 2 can be written in this syntax as follows:
(∀?t∈Time)?t≥6 ∧?t≤10 ⇒
(∃?c∈Commitment) hasService(?c,?s) ∧
hasServiceType(?s,‘x’) ∧ hasAmount(?s,3)
Unary predicates and named sets in these expressions (P,Q,X,Y,Commitment,
Time,etc) are RDFS or OWL classes,while binary predicates (p,q,hasService,
hasAmount,etc) are RDFS or OWL properties.When CIF is used to express commit-
ments,most of these terms will come fromdomain-specific service ontologies —exam-
ples are given in Section 5.Due mainly to the addition of explicit universal and existen-
tial quantifiers,and nested implications,CIF constraints are not expressible in SWRL
as it stands,so we have defined CIF/SWRL as an extension of SWRL:we reuse the
implication structure from SWRL,but allow for nested quantified implications within
the consequent of an implication.Compared to the SWRL syntax in [9],this simply
adds the quantifiers and supports nested implications.Note that the innermost-nested
implication has an empty body as it is always of the form “true ⇒...”.(In the above
syntax this is implicit;the following abstract syntax,and the RDF syntax given in the
appendix make this explicit.)
Figure 2 shows the CIF extensions to the abstract syntax given in SWRL and OWL
documentation [9],using the same EBNF syntax.A constraint expression retains
The term “constraint” is often used rather freely;in this paper we use the term for logical
expressions within the scope of Colan —see [12] for broader discussion of the relationship
between rules and constraints.
the URIreference and annotation syntax features from SWRL so as to allow
statements to be made about the constraints themselves (see Section 4.1).Note that
nesting is handled by extending the original SWRLgrammar,allowing a constraint
to appear recursively inside a consequent.
constraint::= ’Implies(’ [ URIreference ] { annotation }
quantifiers antecedent consequent ’)’
antecedent::= ’Antecedent(’ { expr } ’)’
consequent::= ’Consequent(’ consexpr ’)’
consexpr::= constraint | { atom }
expr::= atom | disjunct | conjunct | negation
disjunct::= ’Or(’ { expr } ’)’
conjunct::= ’And(’ { expr } ’)’
negation::= ’Not(’ expr ’)’
quantifiers::= ’Quantifiers(’ { q-atom } ’)’
q-atom::= quantifier ’(’ q-var q-set ’)’
quantifier::= ’forall’ | ’exists’
q-var::= I-variable
q-set::= description
Fig.2.CIF/SWRL abstract syntax in EBNF
The definition of antecedent is extended from SWRL to allow combinations
of disjunction,conjunction,and negation expressions.In the simplest case where an
antecedent is a conjunction of atoms,the syntax allows omission of an explicit And
structure —the “and” is implicit (as in the SWRL syntax).However,disjunctions and
negations are always explicit,as are any conjunctions within them.It is worth noting
that a consequent can be only a conjunction —CIF/SWRL does not allow disjunction
or negation here.
As defined by the SWRL EBNF,an atom may be a unary predicate (for exam-
ple,P(I-variable(x)) or a binary predicate (for example,q(I-variable(y)
I-variable(z))).The only other notable additional syntax is the quantifiers
structure,a list of individual quantifier expressions,each of which contains a reference
to a SWRL I-variable and an OWL description.So,in the informal expression “?x
∈ X” x is an I-variable and X is an OWL/RDFS class identifier.
The example commitment c2 re-cast into the abstract syntax is shown in Figure 3.
Note the empty antecedent in the innermost-nested implication.
The RDF syntax for CIF/SWRL is summarised in the appendix.
4 An Ontology for Representing Soft CSPs
Before presenting our soft CSP ontology,we examine common features of soft CSPs in
the literature and in practical implementations,in order to identify the minimal features
required of the ontology.
Quantifiers(forall(I-variable(t) Time))
greaterThanOrEqual(I-variable(t) 6)
lessThanOrEqual(I-variable(t) 10))
Quantifiers(exists(I-variable(c) Commitment))
hasService(I-variable(c) I-variable(s))
hasService(I-variable(s) ’x’)
hasAmount(I-variable(s) 3)))))
Fig.3.Example constraint shown in the CIF/SWRL abstract syntax
Soft constraints can be represented and implemented in a variety of ways,depend-
ing on language and system used.In this section we look at a number of CSP-solving
frameworks (based on Prolog and Java),and describe ways in which we can model
soft constraints using the features available in those frameworks.We then give a brief
overview of some of the soft constraint literature.
Prolog Implementations In many Prolog implementations,the issue of soft constraints
can be modelled with reification.Reification is the attachment of a boolean value to each
constraint.If a constraint is satisfied,then the boolean value is set to true,otherwise it is
set to false.This means that it is possible to reason about the constraints,by reasoning
about these boolean values.
Given an unsatisfiable problem,the aimthen is to find the best subset of simultane-
ously satisfiable constraints (i.e.true values),by utilising the attached boolean values.
These values themselves can then form the basis for a meta-level CSP,the solu-
tion to which is an assignment of reification values to constraints at the lower level.
,GNU Prolog
and SWI Prolog
all provide a systemof reification.
Java Implementations In Java,two dominant constraint libraries are Java Constraint
Library (JCL)
and Choco
The JCL attaches a floating point number to each tuple of a constraint rated from
0.0 (important) to 1.0 (not important),so each outcome pairing is given a value showing
its preference as a solution.When solutions are returned fromthe solver they are given
a ‘score’ dependent on what tuple has been chosen.These may be used to prioritise the
solutions dependent on preferences.
This method can easily model the reification described in the Prolog systems.If we
add ‘0’ to each domain of possible values for each variable,we can class this as a ‘not
applied’ value for that variable (i.e.if the variable is assigned to 0,we take it to be
not satisfied).We can mark a constraint tuple where an assigned value is 0 as 1.0 (i.e.
not important),and other possible values as anywhere between 0.0 to 0.9;therefore the
preference will to be find a value other than 0 for that constraint (i.e.satisfy the con-
straint).Obviously this requires some work-arounds when zero value assignments are
required for specific values,but in the case of the commitment management examples
we have been investigating,this method has proved satisfactory.
Choco is a system for solving constriants,also written in Java.It is a library for
constraint satisfaction problems (CSPs),constraint programming (CP) and explanation-
based constraint solving that is built upon an event-based propagation mechanism.The
type of constraints that can be handled by Choco are arithmetic constraints (equal-
ity,difference,comparisons and linear combination),boolean and user-defined N-ary
constraints.The propagation engine maintains arc-consistency for binary constraints
throughout the solving process,while for n-ary constraints,it uses a weaker propaga-
tion mechanismwith a forward checking algorithm.Choco uses a systemof explanation
based solving
.Using this method,a constraint programcan describe why certain deci-
sion were taken (i.e.why variable xcannot take the value a) and so showwhy a problem
fails.This information can then be used to find subsets of satisfiable constraints within
the given set.
Soft Constraints in the Literature A number of people in the literature look at the
scoring,or ordering,of constraints in CSP solving in two main ways [2]:
– Assigning values to each possible tuple in a constraint.
– Assigning a value to the actual constraint itself.
There are a number of ways that these two methods are modelled.Fuzzy CSPs [8]
allow constraint tuples to have an associated preference (1 = best,0 = worst).Again,as
described in the Java Constraint Library section,we can still model (and have modelled)
partial CSPs using this method,by adding a tuple with 0 values to the domain of possible
values,and assigning this a ‘1.0’ preference (i.e.worst outcome).Similarly weighted
CSPs [4] assign preference,but the value given with each tuple is associated with a cost.
The main factor in these types of CSP is that a value is associated with the individual
tuples in a constraint,not the actual general constraint itself.
Freuder and Wallace [7] talk more in terms of the actual constraints themselves,
and relaxing them.They talk about sets of solutions,rather than the actual individual
solutions to each variable.They then talk about a partial ordering of solutions,where
the solutions are ordered by a given distance metric.
4.1 The CSP Ontology
We were interested in developing a well formed means of representing a set of one (or
more) constraints that,when combined,form a single (soft) CSP,the ultimate goal be-
ing to facilitate interchange of information between a CSP problem constructor and an
appropriate solver.The solver would process the problem and return to the constructor
zero or more solutions,each solution identifying those constraints that are satisfied and
those that are violated by that solution.This would then allow the CSP constructor to
decide itself which solution to select.
As discussed in Section 4,soft CSP solvers typically allow each constraint to be
assigned a utility value,defined as a floating point number with a value ranging from
0 to 1 inclusive.These values represent the significance,or importance,of that con-
straint with respect to the other constraints comprising the CSP.Essentially,this value
represents the degree of softness of each constraint,with a higher number implying a
lower softness,and therefore a greater desirability to satisfy that constraint.However,
depending upon the strategy employed by the CSP solver,a constraint with a lower
utility value may still be satisfied in preference to violating another constraint with a
higher utility value.
From the preceding discussion,it is clear that a utility value is not an intrinsic part
of a constraint itself,rather it can be viewed as a kind of annotation on a constraint,
with respect to a particular CSP (set of constraints).Similarly,the status of a constraint
in terms of whether it is satisfied or not can be seen as an annotation of that constraint
with respect to a particular solution.Therefore,we decided to create a separate ontology
to represent a CSPs,independent of the (CIF/SWRL) representation of the individual
constraints themselves.While for our practical purposes the ontology would be mainly
used to annotate CIF/SWRL constraints,in principle it should be usable with other
constraint and rule representations.
Figure 4 is a graphical depiction of the OWL CSP ontology,which is expressed in
OWL DL and SWRL (classes are drawn as ovals,primitive data types as rectangles,and
properties are arcs going from the domain and pointing to the range of that property).
Initially,a CSP constructor would create an instance of a ConstraintProblem
with one,or more,instances of ValuedConstraint.Each ValuedConstraint
is assigned a utility value (real number) with the actual constraint expressed using
CIF/SWRL.At this point the constructor would have only a representation of the CSP
itself;there would be no instances of the Solution class.Only once the CSP has been
passed onto a solver will any instances of Solution be created (or not,if no solution
can be found).
The properties satisfies and violates are used to represent the fact that a
particular solution instance satisfies a particular constraint,or not.Clearly,the use of
these properties must be disjoint between the same instances:a given constraint can
only be satisfied or violated with respect to a given solution.OWL DL does not enable
us to enforce this check
,so we define a rule to enforce data integrity in this case.
The first two solutions fromFigure 1 are represented in triple formas follows using
the CSP ontology:
<ex:soln1> <csp:satisfies> <ex:c1>
<ex:soln1> <csp:satisfies> <ex:c2>
<ex:soln1> <csp:satisfies> <ex:c3>
<ex:soln1> <csp:violates> <ex:N>
Disjoint property axioms are expected to be available in OWL 1.1,which is still decidable:
Fig.4.Graph of the CSP ontology
<ex:soln2> <csp:violates> <ex:c1>
<ex:soln2> <csp:violates> <ex:c2>
<ex:soln2> <csp:satisfies> <ex:c3>
<ex:soln2> <csp:satisfies> <ex:N>
While adding SWRL rules to a DL knowledge base can make inference undecid-
able [10],this particular rule is within the DL-safe subset of SWRL (as the disjointness
is imposed on named ValueConstraints rather than any possible ones).There-
fore,it is still possible to have decidable reasoning support for our OWL DL +SWRL
version of the CSP ontolgy.
5 Demonstrator Systems
To demonstrate reuse of the commitment management service it has been applied in two
distinct domains:e-commerce and e-response.In the first domain,the CMS has been
used in the context of a multimedia service provisioning demonstrator systemdeveloped
as part of the Conoise-G project [14].A customer wishes to subscribe to a package of
multimedia services for their mobile device — a screenshot from the demonstrator,
including a PDA simulator,is shown in Figure 5.A service ontology defines available
service types and characteristics,from which the user can select their requirements via
their user-agent.These requirements are then posted to the network of service-providing
agents via a yellow pages,inviting agents to bid to provide the elements of the required
Fig.5.The Conoise-G virtual organisation demonstrator system.
Here,a virtual organisation is entirely agent-mediated:in response to a call for bids,
an agent reasons about its available resources and commitments on those resources,and
decides whether and what to bid.If it is already representing a virtual organisation,the
available resources and existing commitments are the combined resources and commit-
ments of the organisation.In making its deliberations,the agent has the option to seek
to recruit other service providers to extend the resources it can provide;it can also opt
to free-up resources by breaking existing commitments as described in Section 2.
Commitments on resources are expressed as constraints on classes defined in a
Conoise-Gmediaontology,which defines all application domain-specific terms for the
multimedia service provisioning scenario.These include the service classes MovieCon-
tent,HtmlContent,PhoneCalls,and TextMessaging,all of which the ontology de-
fines to be (indirect) sub-classes of the generic Conoise-G ServiceProfile class (based
on DAML-S).The Conoise-G demonstrator is built on the FIPA standard agent plat-
;the content of all inter-agent communication is RDF.The CMS is implemented
using the Java Constraint Library;RDF processing is done using Jena2
,by means
of which the RDF transport format of the CSPs is converted into the JCL native CSP
format for solving.
The scenario for our second application domain —e-response —is a fictitious dis-
aster in the city of London,UK.
Here,the services upon which commitments need
Details are available at:http://e-response.org/
to be managed are physical entities such as fire engines,ambulances,police units,etc.
Like the multimedia scenario,these are defined in a domain-specific service ontology,
and CIF/SWRL constraints express commitments over them (for example,“commit
10 fire engines to a fire incident at Bartholemew’s Hospital,from 10am,for an esti-
mated duration of 5 hours”).A key difference to the e-commerce scenario is that this
is human-mediated:human decision-makers need to be presented with possible com-
mitment management solutions for them to make informed choices.This requires that
the CMS be interfaced with the Compendiumissue-mapping software that provides the
main user interface,illustrated in Figure 6.
Fig.6.The e-response virtual organisation demonstrator system.
Together,these two demonstrators illustrate a range of application dimensions for
the reuse of the CMS,in managing commitment knowledge in both autonomous,agent-
mediated virtual organisations,and human-mediated decision-making.In both cases,
commitments are expressed using CIF/SWRL against pre-existing service ontologies.
6 Discussion and Conclusion
In this paper we presented a set of components comprising a reusable commitment man-
agement service for agents operating in virtual organisations.The components build on
the Semantic Web architecture,so allowing the management of commitments over Se-
mantic Web services (and indeed any service defined using OWL or RDFS).Some
of the components have more general applicability than commitment management:
CIF/SWRL and the soft CSP ontology are reusable for any application of CSP and
soft CSP-solving in a Semantic Web context (one such application is described in [12]).
While there exists an XML-based proposal for representing CSPs [3],to the best of our
knowledge our proposal is the first CSP interchange format founded on RDF and OWL.
Note that,while the CSP ontology is designed to work with CIF as the constraint
representation,it is conceivable that other constraint and rule representations could be
used as the values of the expression properties of ValueConstraints.As work con-
tinues on standardising Semantic Web rule and constraint languages
,we will consider
suitable extensions to the CSP ontology.
The SWRL FOL proposal to extend SWRL to full first-order logic
shares many
of the features we earlier proposed for CIF/SWRL.While,at the time of writing,the
SWRL FOL proposal lacks an RDF syntax,we anticipate it would not be hard to fully
align CIF/SWRL with SWRL FOL.The main differences are in the syntactic form for
the quantifier parts of expressions,a more expressive consequent (SWRL FOL allows
disjunction and negation here),and a more complex syntax for simple conjunctions
(SWRL FOL opts not to follow the SWRL “list format” for these).
Currently,work on the e-response scenario is ongoing,and our focus is moving onto
effective integration of human-mediated and agent-mediated decision processes.
Acknowledgments This work is supported under the Advanced Knowledge Technolo-
gies (AKT) Interdisciplinary Research Collaboration (IRC),which is funded by the
UK Engineering and Physical Sciences Research Council (EPSRC) under grant num-
ber GR/N15764/01.The AKT IRC comprises the Universities of Aberdeen,Edinburgh,
Sheffeld,Southampton,and the Open University.See also:http://www.aktors.org
The commitment management service was developed in the context of the Conoise
and Conoise-Gprojects,involving the Universities of Aberdeen,Cardiff,and Southamp-
ton,and British Telecom,and funded by the DTI/Welsh e-Science Centre,and BT.We
are grateful to Gareth Shercliffe and Patrick Stockreisser for their work on the Conoise-
G demonstrator user interface.See also:http://www.conoise.org
1.N.Bassiliades and P.M.D Gray.CoLan:a Functional Constraint Language and Its Imple-
mentation.Data and Knowledge Engineering,14:203–249,1994.
2.Stefano Bistarelli,H´el`ene Fargier,Ugo Montanari,Francesca Rossi,Thomas Schiex,and
G´erard Verfaillie.Semiring-based CSPs and Valued CSPs:Basic properties and comparison.
In Michael Jampel,Eugene Freuder,and Michael Maher,editors,Over-Constrained Systems,
pages 111–150.Springer-Verlag LNCS 1106,August 1996.
3.F.Boussemart,F.Hemery,and C.Lecoutre.Description and representation of the problems
selected for the first international constraint satisfaction solver competition.Technical report,
CRIL,Universit´e d’Artois,2005.
4.Ken Brown.Soft consistencies for weighted csps.In Proceedings of Soft’03:5th Interna-
tional Workshop on Soft Constraints,Kinsale,Ireland,September 2003.
5.S.Chalmers,A.D.Preece,T.J.Norman,and P.Gray.Commitment management through
constraint reification.In 3rd International Joint Conference on Autonomous Agents and
Multi Agent Systems (AAMAS 2004),pages 430–437,2004.
6.I.Foster,N.R.Jennings,and C.Kesselman.Brain meets brawn:Why Grid and agents
need each other.In Proceedings of the Third International Joint Conference on Autonomous
Agents and Multi-Agent Systems,pages 8–15,2004.
7.Eugene C.Freuder.Partial Constraint Satisfaction.In Proceedings of the Eleventh Interna-
tional Joint Conference on Artificial Intelligence,IJCAI-89,Detroit,Michigan,USA,pages
8.Hans W.Guesgen and Anne Philpott.Heuristics for solving fuzzy constraint satisfaction
problems.In 2nd New Zealand Two-Stream International Conference on Artificial Neural
Networks and Expert Systems (ANNES ’95),1995.,1995.
9.I.Horrocks,P.F.Patel-Schneider,H.Boley,S.Tabet,B.Grosof,and M.Dean.SWRL:A
Semantic Web rule language combining OWL and RuleML.Technical report,W3C,2004.
10.Ian Horrocks and Peter Patel-Schneider.A proposal for an OWL Rules Language.In Thir-
teenth International World Wide Web Conference (WWW2004).ACM,2004.
11.K.Hui,S.Chalmers,P.Gray,and A.Preece.Experience in using RDF in agent-mediated
knowledge architectures.In L.van Elst,V.Dignum,and A.Abecker,editors,Agent-Mediated
Knowledge Management (LNAI 2926),pages 177–192.Springer-Verlag,2004.
12.C.McKenzie,P.Gray,and A.Preece.Expressing fully quantified constraints in CIF/SWRL.
In G.Antoniou and H.Boley,editors,Rules and Rule Markup Languages for the Semantic
Web (RuleML 2004),pages 139–154.Springer-Verlag,2004.
Nguyen,V.Deora,J.Shao,W.A.Gray,and N.J.Fiddian.CONOISE:Agent-based forma-
tion of virtual organisations.Knowledge-Based Systems,17:103–111,2004.
P.M.D.Gray,P.J.Stockreisser,G.Shercliff,J.Shao,W.A.Gray,N.J.Fiddian,and S.
Thompson.Agent-based virtual organisations for the grid.In Proc 1st International Work-
shop on Smart Grid Technologies,2005.
Appendix:CIF/SWRL RDF Syntax
To support publishing and interchange of CIF constraints in the Semantic Web context,
we provide an RDF/XML syntax as an extension to the one given for SWRL.The full
RDF Schema for the CIF/SWRL syntax is available at the project website
;here we
merely summarise the necessary extensions to the SWRL RDF syntax:
– We define a new rdfs:Class Constraint,with two associated properties:
hasQuantifiers and hasImplication.The range of the former is an RDF
list (of quantifier structures) and the range of the latter is a ruleml:Imp.
– We define the parent class Quantifier with sub-classes Forall and Exists.
Two properties var and set complete the implementation of the q-atom from
the abstract syntax.The range of both is an RDF resource:in the case of var this
will be a URIref to a SWRL variable,while for set it will identify an OWL/RDFS
– Note that the SWRL RDF syntax allows the body of an implication to be any RDF
list,so it already allows the nested inclusion of a Constraint.
– We define OrExpressionand AndExpressionas sub-classes of rdf:List,
and a Negation class that has a swrl:argument1 property to point to the
negated atom.
The RDF/XML for the constraint c2 is shown in Figure 7.
<cif:Constraint rdf:about=“#c2”/>
<cif:hasQuantifiers rdf:parseType=“Collection”>
<cif:var rdf:resource=“#t”/>
<cif:set rdf:resource=“&schedule;#Time”/>
<swrl:body rdf:parseType=“Collection”/>
<swrl:propertyPredicate rdf:resource=“&swrlb;#greaterThanOrEqual”/>
<swrl:argument1 rdf:resource=“#t”/>
<swrl:argument2 rdf:datatype=“&xsd;#int”/>6</swrl:argument2/>
<swrl:propertyPredicate rdf:resource=“&swrlb;#lessThanOrEqual”/>
<swrl:argument1 rdf:resource=“#t”/>
<swrl:argument2 rdf:datatype=“&xsd;#int”/>10</swrl:argument2/>
<swrl:head rdf:parseType=“Collection”>
<cif:hasQuantifiers rdf:parseType=“Collection”>
<cif:var rdf:resource=“#c”/>
<cif:set rdf:resource=“&schedule;#Commitment”/>
<swrl:head rdf:parseType=“Collection”>
<swrl:classPredicate rdf:resource=“&schedule;#hasService”/>
<swrl:argument1 rdf:resource=“#c”/>
<swrl:argument2 rdf:resource=“#s”/>
<swrl:classPredicate rdf:resource=“&schedule;#hasServiceType”/>
<swrl:argument1 rdf:resource=“#s”/>
<swrl:argument2 rdf:resource=“&service;#x”/>
<swrl:classPredicate rdf:resource=“&schedule;#hasAmount”/>
<swrl:argument1 rdf:resource=“#c”/>
<swrl:argument2 rdf:datatype=“&xsd;#int”/>3</swrl:argument2/>
Fig.7.RDF/XML for the constraint (commitment) c2